The kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. It is commonly used in statistics and machine learning to analyze and visualize data. In this blog post, we will explore how to perform kernel density estimation using the statsmodels library in Python.

Installation

Before we get started, we need to install the statsmodels library. You can use the following command to install it via pip:

pip install statsmodels

Importing the Required Libraries

To perform kernel density estimation, we need to import the necessary libraries. In this case, we will only need the statsmodels library:

import statsmodels.api as sm

Loading the Data

Before performing kernel density estimation, we need to load the data that we want to analyze. In this example, let’s assume we have a list of heights of individuals:

heights = [165, 172, 168, 170, 176, 180, 172, 175, 170, 168, 165, 180, 178, 175]

Estimating the Kernel Density

To estimate the kernel density, we can use the KernelDensity class provided by statsmodels. This class provides various kernel density estimation methods, such as Gaussian, Epanechnikov, and Biweight.

In this example, let’s use the Gaussian kernel:

kde = sm.nonparametric.KDEUnivariate(heights)
kde.fit(kernel='gau')

Plotting the Estimated Density

After estimating the kernel density, we can plot the estimated density function using the .plot() method:

kde.plot()

This will display a plot showing the estimated density function of the given data.

Customizing the Plot

We can also customize the plot by adding labels, a title, and adjusting the plot style. Here’s an example of customizing the plot:

import matplotlib.pyplot as plt

plt.figure(figsize=(8, 6))
plt.title('Kernel Density Estimation')
plt.xlabel('Height')
plt.ylabel('Density')
kde.plot(color='red', linestyle='dashed')
plt.grid(True)
plt.show()

By adding these lines of code, we are setting the figure size, adding a title and labels, changing the color and linestyle, and enabling gridlines in the plot.

Conclusion

In this blog post, we learned how to perform kernel density estimation using the statsmodels library in Python. We covered the installation process, loading the data, estimating the kernel density, and plotting the estimated density function.

Kernel density estimation is a powerful tool for analyzing the distribution of data and visualizing the probability density function. With statsmodels, it becomes easy to perform kernel density estimation in Python.

Remember to experiment with different kernel types and plot customizations to achieve the desired results for your data analysis tasks.